A SystemBC proxy malware botnet of more than 1,570 hosts, believed to be corporate victims, has been discovered following an investigation into a Gentlemen ransomware attack carried out by a gang affiliate.
The Gentlemen ransomware-as-a-service (RaaS) operation emerged around mid-2025 and provides a Go-based locker that can encrypt Windows, Linux, NAS, and BSD systems, and a C-based locker for ESXi hypervisors.
The U.S. Army 1st Cavalry Division has completed the latest phase of its counter drone experimentation, a live-fire exercise from April 7–9 testing cUAS systems for its “Golden Shield” counter-drone concept for an armored formation. This significant step in the division’s Pegasus Charge initiative incorporated autonomous cUAS battlefield effectors for the first time, advancing efforts to protect U.S. forces from the growing threat of small unmanned aerial systems. Exercise Golden Shield integrated advanced sensors, kinetic and non-kinetic effectors and command-and-control systems to create an autonomous cohesive defense against small UAS. The effort, led by the 1st Cavalry Division in collaboration with Army DEVCOM and industry partners, aims to enhance the protection of armored vehicles and their crews while maneuvering. The system links sensors and weapons on tactical vehicles to automatically detect, track and engage threats, significantly shortening the sensor-to-shooter timeline and reducing cognitive load.
“The intent is to take these systems we tested this week and begin to integrate them within our armored formations’ training,” said Maj. Kevin Correa, 1st Cavalry Division’s air and missile defense chief. “In that way, we are able to fully exercise not only the systems, but the tanker’s ability to manage these systems while conducting their normal operations.”
“The future is formation-based layered protection, and this is the start of that,” said Alfred Grein, executive director for Research and Technology Integration for the U.S. Army Capabilities Development Command Ground Vehicle Systems Center. “Some (of the systems) are more mature than others. But understand that’s part of why we do experiments to determine what we think is ready to hand-off to Soldiers in the field environment.”
Online now:(Cell Metabolism 36, 2402–2418.e1–e10; November 5, 2024)
Online now: (Cell Metabolism36, 2402–2418.e1–e10; November 5, 2024)
In the originally published article, due to figure preparation mistakes, there were errors in Figures 2, 3, and S9. Specifically, the line legends in Figure 2J were accidentally lost during the creation of the figure using AI software, the marker positions for the β-actin bands in Figure 3J were incorrectly labeled, the H&E staining image of the wild-type mouse DOX+17-OH PREG treatment group in Figure S9A was erroneously pasted during figure compilation, and the IHC staining image of the liver ischemia-reperfusion treatment group in Figure S9I was flipped during copying. We apologize for these oversights that occurred during the many revisions.
Because certain western bands were not clear, we corrected Figures 2C and 3G with full-membrane original data. In addition, CD36 appears to be over 100 kDa in Figure S10S, whereas it is consistently between 70 and 100 kDa in all other figures. We have previously encountered similar problems with certain proteins with a little difference in molecular weight, and we have solved this issue by using other lysis buffers. Therefore, we used another lysis buffer (epizyme CAT: PC201) to examine whether there is a consistent phenotype of CD36 between 70 and 100 kDa. As expected, we detected a significant decrease of CD36 located within 70–100 kDa upon IR, Dox, and MCDD treatment, which was consistent with our published data of CD36 above 100 kDa. Because the major CD36 band should appear at approximately 88 kDa based on numerous studies, we have removed the original data from Figure S10S and presented the corrected bands in Figure S10U to avoid confusion.
The past and future are the same thing | feynman on time symmetry.
Discover one of physics’ most mind-bending secrets: the fundamental laws of nature don’t know which way time flows! In this exploration of Feynman’s ideas on time symmetry, we dive deep into how the equations of physics work equally well forwards and backwards, why positrons are electrons moving backward through time, and how the Wheeler-Feynman absorber theory suggests the future might influence the past.
From billiard balls to quantum mechanics, from Maxwell’s equations to the mystery of why we remember yesterday but not tomorrow, this video unravels the beautiful symmetry hidden beneath our everyday experience of time.
Topics Covered: • Time symmetry in fundamental physics • Positrons as electrons traveling backward in time • Wheeler-Feynman absorber theory • The thermodynamic arrow of time • Path integral formulation and quantum mechanics • Why time appears to flow in one direction • CP violation and the weak nuclear force.
Perfect for physics enthusiasts, students, and anyone curious about the nature of time and reality.
⚠️ DISCLAIMER: This is AI-generated content created in the style of Richard Feynman’s teaching approach. The script synthesizes information from various sources about Feynman’s work and ideas in theoretical physics, including his lectures, published papers, and documented contributions to quantum electrodynamics and time-symmetric theories. While based on authentic concepts from Feynman’s career, this is an educational interpretation and not actual recorded material from Richard Feynman.
In this presentation, Raia Hadsell, VP of Research at Google DeepMind and AI Ambassador for the United Kingdom, opens AIE Europe and explores what’s open in Frontier AI and the future of intelligence by focusing on advancements beyond standard large language models. She categorizes these innovations into three key areas:
00:00 Introduction. 05:05 Advanced Embedding Models: Raia discusses the importance of embedding models for fast retrieval and recognition, similar to how the human brain uses ‘Jennifer Aniston cells’ to identify concepts across modalities. She highlights Gemini Embeddings 2, a fully omnimodal model that processes text, video, and audio into unified semantic vectors. 09:53 AI for Weather Forecasting: The team has developed revolutionary models for atmospheric prediction, moving away from traditional physics simulations. Notable breakthroughs include: 11:00 GraphCast: A spherical graph neural network that provides accurate 15-day weather forecasts. 12:47 GenCast: A probabilistic model that offers higher efficiency and accuracy (97% of the time compared to gold-standard benchmarks). 13:51 FGN: A functional generative network that directly predicts cyclone behavior, which is currently being utilized by the US National Hurricane Center. 14:35 World Models: Hadsell introduces Genie, a project focused on creating interactive, real-time environments. Starting from Genie 1 (2D platformers) and progressing to Genie 3, these models allow users to create and interact with high-quality, 3D photorealistic worlds. These environments demonstrate capabilities like memory, consistency, and the ability to be dynamically prompted by the user to change the surroundings in real-time.
Life of leonard susskind;leonard susskind physics.
*Description:* Can scientists really simulate a full human brain now? In this video, we explore the latest study claiming that supercomputers may soon be powerful enough to simulate the human brain. We break down how this new method works, why previous brain simulation projects failed, what makes this new research different, and the big ethical questions that come with it. Is this the future of neuroscience and artificial intelligence, or are we still far from creating a true digital human mind? Watch till the end to understand the science in simple words.
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A novel imaging method captures ultrafast events with unprecedented detail by combining laser encoding and AI reconstruction. Researchers have introduced a new imaging method that reveals far more detail about ultrafast events in the microscopic world than earlier approaches. The technique allows
For this episode, I’m joined by Rick Tumlinson, co-founder of the Space Frontier Foundation and one of the most influential figures in the commercial space industry.
In this episode, we slice the conversation into four categories: the social history of the space movement and how we got here; the business of space and the astropolitics shaping who controls the final frontier; the genetics and ethics of humanity becoming a multi-planetary species; and the deeper philosophy of why leaving Earth isn’t just raw and blind ambition but something closer to destiny (for some people).
Timestamps: 0:00 Social History. 30:19 Business and Astropolitics. 45:20 Genetics and Ethics. 56:02 Philosophical.
Inverse lithography takes a radically different approach. Instead of starting with the desired circuit pattern and tweaking it to compensate for optical distortions, ILT works backwards. It asks: “What mask pattern would produce the exact shape we want after the light does its distorting work?” It’s like designing a funhouse mirror that makes your reflection look perfectly normal.
What’s particularly elegant are the “model-driven deep learning” approaches, which combine the physics of how light actually behaves with AI’s pattern-recognition abilities. Rather than making the AI learn optics from scratch, these hybrid methods embed the known laws of physics into the learning process, creating solutions that are both fast and physically accurate.
World shares its vision for the next chapter of proof of human, and the partners, products, and protocol upgrades that accelerate every human in the age of AI.
